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Corporate governance, namely the relationship between the ownership and control of firms, takes on new dimensions in the case of international joint ventures operating in the special context of China. The present study contributes a new examination of this relationship, firstly through its conceptual refinement, and secondly through original empirical research. It develops the concept of ownership as suited to joint ventures, in which account is taken of non-capital resourcing by foreign and Chinese partners.

Die ISO (International Organization for Standardization)-Norm "Geometrische Produktspezifikation" (GPS) definiert die Linienprofiltoleranz. Die Linie ist entweder eine extrahierte Linie eines Bauteils – spezifiziert durch eine Schnittebene – oder eine abgeleitete Linie. Diese Definition kommt in der Praxis kaum zur Anwendung. Auch können so funktional erforderliche Toleranzinformationen nicht vollständig beschrieben werden. Die Autoren empfehlen in diesem Fachbeitrag eine neue Systematik, basierend auf der Analyse von Bauteilfunktionen, um die Definition der Linienprofiltoleranz zu ergänzen und zu erweitern. Dazu zeigt der Artikel Praxisbeispiele und es wird eine erweiterte Definition des Linienprofils erstellt.
 
According to the ISO standard on Geometrical Product Specification (GPS), the tolerance symbol of line profile describes a derived middle line or an extracted line of a part specified by an intersection plane. This definition is rarely applied in practice and some functionally required tolerance information of parts made of sheet metal cannot be described by the current ISO definition. This article suggests a new holistic methodology to complete and extent the definition of line profile based on the analysis of product functions and gives practical examples. As a result, this paper presents an enhanced definition of line profile.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14$^{+}$ monocytes, CD16$^{+}$ neutrophils, and naive CD4$^{+}$ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of $\textit{cis}$-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk. ; This work was predominantly funded by the EU FP7 High Impact Project BLUEPRINT (HEALTH-F5-2011-282510) and the Canadian Institutes of Health Research (CIHR EP1-120608). The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 282510 (BLUEPRINT), the European Molecular Biology Laboratory, the Max Planck society, the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017', SEV-2012-0208 and Spanish National Bioinformatics Institute (INB-ISCIII) PT13/0001/0021 co-funded by FEDER ""Una Manera de hacer Europa". D.G. is supported by a "la Caixa"-Severo Ochoa pre-doctoral fellowship, M.F. was supported by the BHF Cambridge Centre of Excellence [RE/13/6/30180], K.D. is funded as a HSST trainee by NHS Health Education England, S.E. is supported by a fellowship from La Caixa, V.P. is supported by a FEBS long-term fellowship and N.S.'s research is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR BRC. The Blood and Transplant Unit (BTRU) in Donor Health and Genomics is part of and funded by the National Institute for Health Research (NIHR) and is a partnership between the University of Cambridge and NHS Blood and Transplant (NHSBT) in collaboration with the University of Oxford and the Wellcome Trust Sanger Institute. The T-cell data was produced by the McGill Epigenomics Mapping Centre (EMC McGill). It is funded under the Canadian Epigenetics, Environment, and Health Research Consortium (CEEHRC) by the Canadian Institutes of Health Research and by Genome Quebec (CIHR EP1-120608), with additional support from Genome Canada and FRSQ. T.P. holds a Canada Research Chair.

China, with its growing population and economic development, faces increasing risks to health from climate change, but also opportunities to address these risks and protect health for generations to come. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate. In 2020, the Lancet Countdown Regional Centre in Asia, led by Tsinghua University, built on the work of the global Lancet Countdown and began its assessment of the health profile of climate change in China with the aim of triggering rapid and health-responsive actions. This 2021 report is the first annual update, presenting 25 indicators within five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. The report represents the contributions of 88 experts from 25 leading institutions in, and outside of, China. From 2020 to 2021, five new indicators have been added and methods have been improved for many indicators. Where possible, the indicator results are presented at national and provincial levels to facilitate local understanding and policy making. In a year marked by COVID-19, this report also endeavours to reflect on China's pathway for a green recovery, ensuring it aligns with the carbon neutrality goal, for the health of the current and future generations.

China, with its growing population and economic development, faces increasing risks to health from climate change, but also opportunities to address these risks and protect health for generations to come. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate. In 2020, the Lancet Countdown Regional Centre in Asia, led by Tsinghua University, built on the work of the global Lancet Countdown and began its assessment of the health profile of climate change in China with the aim of triggering rapid and health-responsive actions. This 2021 report is the first annual update, presenting 25 indicators within five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. The report represents the contributions of 88 experts from 25 leading institutions in, and outside of, China. From 2020 to 2021, five new indicators have been added and methods have been improved for many indicators. Where possible, the indicator results are presented at national and provincial levels to facilitate local understanding and policy making. In a year marked by COVID-19, this report also endeavours to reflect on China's pathway for a green recovery, ensuring it aligns with the carbon neutrality goal, for the health of the current and future generations.

Using the data sets taken at center-of-mass energies above 4 GeV by the BESIII detector at the BEPCII storage ring, we search for the reaction e(+)e(-) -> gamma(ISR) X(3872) -> gamma(ISR)pi(+)pi(-) J/psi via the Initial State Radiation technique. The production of a resonance with quantum numbers J(PC) = 1(++) such as the X(3872) via single photon e(+)e(-) annihilation is forbidden, but is allowed by a next-to-leading order box diagram. We do not observe a significant signal of X(3872), and therefore give an upper limit for the electronic width times the branching fraction Gamma B-X(3872)(ee)(X(3872) -> pi(+)pi(-) J/psi) < 0.13 eVat the 90% confidence level. This measurement improves upon existing limits by a factor of 46. Using the same final state, we also measure the electronic width of the psi(3686) to be Gamma(psi)(ee)(3686) ee = 2213 +/- 18(stat) +/- 99(sys) eV. ; Funding: The BESIII collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. This work is supported in part by the National Key Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contract Nos. 11125525, 11235011, 11322544, 11335008, 11425524; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contract Nos. 11179007, U1232201, U1332201; CAS under Contract Nos. KJCX2-YW-N29, KJCX2-YW-N45; 100 Talents Program of CAS; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Contract No. CRC-1044; Seventh Framework Programme of the European Union under Marie Curie International Incoming Fellowship Grant Agreement No. 627240; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; Russian Foundation for Basic Research under Contract No. 14-07-91152; U.S. Department of Energy under Contract Nos. DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118; U.S. National Science Foundation; University of Groningen (RuG) and the Helmholtzzentrum fur Schwerionenforschung (GSI), Darmstadt; WCU Program of National Research Foundation of Korea under Contract No. R32-2008-000-10155-0.

Background Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development. Methods We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate. Findings Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY rates due to several high-burden NCDs (including osteoarthritis, drug use disorders, depression, diabetes, congenital birth defects, and skin, oral, and sense organ diseases) either increased or remained unchanged, leading to increases in their relative ranking in many geographies. From 2005 to 2015, HALE at birth increased by an average of 2·9 years (95% uncertainty interval 2·9–3·0) for men and 3·5 years (3·4–3·7) for women, while HALE at age 65 years improved by 0·85 years (0·78–0·92) and 1·2 years (1·1–1·3), respectively. Rising SDI was associated with consistently higher HALE and a somewhat smaller proportion of life spent with functional health loss; however, rising SDI was related to increases in total disability. Many countries and territories in central America and eastern sub-Saharan Africa had increasingly lower rates of disease burden than expected given their SDI. At the same time, a subset of geographies recorded a growing gap between observed and expected levels of DALYs, a trend driven mainly by rising burden due to war, interpersonal violence, and various NCDs. Interpretation Health is improving globally, but this means more populations are spending more time with functional health loss, an absolute expansion of morbidity. The proportion of life spent in ill health decreases somewhat with increasing SDI, a relative compression of morbidity, which supports continued efforts to elevate personal income, improve education, and limit fertility. Our analysis of DALYs and HALE and their relationship to SDI represents a robust framework on which to benchmark geography-specific health performance and SDG progress. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives for all countries along the development continuum. Funding Bill & Melinda Gates Foundation. ; We would like to thank the countless individuals who have contributed to the Global Burden of Disease Study 2015 in various capacities. The data reported here have been supplied by the US Renal Data System (USRDS). Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Collection of these data was made possible by the US Agency for International Development (USAID) under the terms of cooperative agreement GPO-A-00-08-000_D3-00. Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. Parts of this material are based on data and information provided by the Canadian institute for Health Information. However, the analyses, conclusions, opinions and statements expressed herein are those of the author and not those of the Canadian Institute for Health information. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4 and 5 (DOIs: 10.6103/SHARE.w1.500, 10.6103/SHARE.w2.500, 10.6103/SHARE.w3.500, 10.6103/SHARE.w4.500, 10.6103/SHARE.w5.500), see Börsch-Supan and colleagues, 2013, for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: number 211909, SHARE-LEAP: number 227822, SHARE M4: number 261982). Additional funding from the German Ministry of Education and Research, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, and OGHA_04-064) and from various national funding sources is gratefully acknowledged. This study has been realised using the data collected by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS. The project is financed by the Swiss National Science Foundation. The following individuals would like to acknowledge various forms of institutional support: Simon I Hay is funded by a Senior Research Fellowship from the Wellcome Trust (#095066), and grants from the Bill & Melinda Gates Foundation (OPP1119467, OPP1093011, OPP1106023 and OPP1132415). Amanda G Thrift is supported by a fellowship from the National Health and Medical Research Council (GNT1042600). Panniyammakal Jeemon is supported by the Wellcome Trust-DBT India Alliance, Clinical and Public Health, Intermediate Fellowship (2015–2020). Boris Bikbov, Norberto Percio, and Giuseppe Remuzzi acknowledge that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN). Amador Goodridge acknowledges funding from Sistema Nacional de Investigadores de Panamá-SNI. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). Lijing L Yan is supported by the National Natural Sciences Foundation of China grants (71233001 and 71490732). Olanrewaju Oladimeji is an African Research Fellow at Human Sciences Research Council (HSRC) and Doctoral Candidate at the University of KwaZulu-Natal (UKZN), South Africa, and would like to acknowledge the institutional support by leveraging on the existing organisational research infrastructure at HSRC and UKZN. Nicholas Steel received funding from Public Health England as a Visiting Scholar in the Institute for Health Metrics and Evaluation in 2016. No individuals acknowledged received additional compensation for their efforts. ; Peer-reviewed ; Publisher Version